Improve dataset card: update task categories, add relevant tags, language, and sample usage

README.md

@@ -1,11 +1,16 @@
 ---
 license: mit
 task_categories:
+- feature-extraction
 - fill-mask
+language:
+- mul
 tags:
 - pretraining
 - encoder
 - multilingual
+- text-classification
+- text-retrieval
 ---
 
 # mmBERT Training Data (Ready-to-Use)
@@ -19,6 +24,129 @@ tags:
 
 This dataset is part of the complete, pre-shuffled training data used to train the [mmBERT encoder models](https://huggingface.co/collections/jhu-clsp/mmbert-a-modern-multilingual-encoder-68b725831d7c6e3acc435ed4). Unlike the individual phase datasets, this version is ready for immediate use but **the mixture cannot be modified easily**. The data is provided in **decompressed MDS format** ready for use with [ModernBERT's Composer](https://github.com/mosaicml/composer) and the [ModernBERT training repository](https://github.com/answerdotai/ModernBERT).
 
+## Sample Usage
+
+Models trained on this dataset can be used for various tasks, including generating multilingual embeddings, masked language modeling, classification, and multilingual retrieval.
+
+### Installation
+```bash
+pip install torch>=1.9.0
+pip install transformers>=4.48.0
+```
+
+### Small Model for Fast Inference (Multilingual Embeddings)
+```python
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-small")
+model = AutoModel.from_pretrained("jhu-clsp/mmbert-small")
+
+# Example: Get multilingual embeddings
+inputs = tokenizer("Hello world! 你好世界! Bonjour le monde!", return_tensors="pt")
+outputs = model(**inputs)
+embeddings = outputs.last_hidden_state.mean(dim=1)
+```
+
+### Base Model for Masked Language Modeling
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-base")
+model = AutoModelForMaskedLM.from_pretrained("jhu-clsp/mmbert-base")
+
+# Example: Multilingual masked language modeling
+text = "The capital of [MASK] is Paris."
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get predictions for [MASK] tokens
+mask_indices = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)
+predictions = outputs.logits[mask_indices]
+top_tokens = torch.topk(predictions, 5, dim=-1)
+predicted_words = [tokenizer.decode(token) for token in top_tokens.indices[0]]
+print(f"Predictions: {predicted_words}")
+```
+
+### Classification Task
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch.nn as nn
+import torch
+
+# Load model for classification
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-base")
+encoder = AutoModel.from_pretrained("jhu-clsp/mmbert-base")
+
+# Add classification head
+class MultilingualClassifier(nn.Module):
+    def __init__(self, encoder, num_classes):
+        super().__init__()
+        self.encoder = encoder
+        self.classifier = nn.Linear(encoder.config.hidden_size, num_classes)
+        self.dropout = nn.Dropout(0.1)
+    
+    def forward(self, input_ids, attention_mask=None):
+        outputs = self.encoder(input_ids, attention_mask=attention_mask)
+        pooled_output = outputs.last_hidden_state[:, 0]  # Use [CLS] token
+        pooled_output = self.dropout(pooled_output)
+        return self.classifier(pooled_output)
+
+# Initialize classifier
+model = MultilingualClassifier(encoder, num_classes=3)
+
+# Example multilingual inputs
+texts = [
+    "This is a positive review.",
+    "Ceci est un avis négatif.",
+    "这是一个中性评价。"
+]
+inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
+predictions = model(**inputs)
+```
+
+### Multilingual Retrieval
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
+import numpy as np
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/mmbert-base")
+model = AutoModel.from_pretrained("jhu-clsp/mmbert-base")
+
+def get_embeddings(texts):
+    inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    # Mean pooling
+    embeddings = outputs.last_hidden_state.mean(dim=1)
+    return embeddings.numpy()
+
+# Multilingual document retrieval
+documents = [
+    "Artificial intelligence is transforming healthcare.",
+    "L'intelligence artificielle transforme les soins de santé.",
+    "人工智能正在改变医疗保健。",
+    "Climate change requires immediate action.",
+    "El cambio climático requiere acción inmediata."
+]
+
+query = "AI in medicine"
+
+# Get embeddings
+doc_embeddings = get_embeddings(documents)
+query_embedding = get_embeddings([query])
+
+# Compute similarities
+similarities = np.dot(doc_embeddings, query_embedding.T).flatten()
+ranked_docs = np.argsort(similarities)[::-1]
+
+print("Most similar documents:")
+for i, doc_idx in enumerate(ranked_docs[:3]):
+    print(f"{i+1}. {documents[doc_idx]} (score: {similarities[doc_idx]:.3f})")
+```
+
 ## Licensing & Attribution
 
 This dataset aggregates multiple open-source datasets under permissive licenses. See individual source datasets for specific attribution requirements.